Image adjustment

ABSTRACT

Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user&#39;s adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

PRIORITY INFORMATION

This application claims benefit of priority of U.S. patent applicationSer. No. 13/036,245, filed Feb. 28, 2011, which claims benefit ofpriority of U.S. Provisional Application Ser. No. 61/351,254, entitled“Methods and Apparatus for Automatic Global Photograph Adjustment” filedJun. 3, 2010, the content of each of which is incorporated by referenceherein in its entirety.

BACKGROUND

1. Technical Field

This disclosure relates generally to image adjustment and, morespecifically, to automatic image adjustments.

2. Description of the Related Art

Adjusting photographs is a tedious process that requires skill and time.The difference between a picture that comes straight from the camera anda carefully adjusted one can be dramatic just by balancing the tones andrevealing the interplay of light. To adjust a photograph, photographersneed to consider the image content and the tonal challenges it presents.Even adjusting contrast and tonal balance is challenging because it musttake into account the photo subject and lighting conditions.

Decision factors in photograph adjusting are often subjective and cannotbe directly embedded into algorithmic procedures. Some photo editingpackages offer automatic adjustment, however, many offer a simpleheuristic that fails to address more complex adjustments that dependupon scene characteristics such as low versus high key, scenes withback-lighting, or other difficult lighting situations. Other packagesmay apply simple rules, such as fixing the black and white points of theimage to the darkest and brightest pixels. Although this may work insimple cases, these approaches fail in more complex examples, in which aphotographer would apply more sophisticated modifications. Because ofthe complexities inherent in photograph adjusting, rule-based automatictechniques for adjusting photographs often fail.

SUMMARY

This disclosure describes techniques and structures that facilitateautomatic global image adjustment. In one embodiment, a new image may beautomatically globally adjusted based on a regression algorithm trainedwith a database of raw and adjusted images. The regression algorithm maygenerate a plurality of curves that may relate one or more parameters,such as tonal parameters, from the raw images to corresponding adjustedimages. In one embodiment, the trained algorithm may automaticallyglobally adjust a new image by performing a weighted combination of theplurality of curves and applying the weighted combination of curves tothe new image. In one embodiment, a new image may be automaticallyglobally adjusted based on a regression algorithm trained on thedatabase of raw and adjusted images and adjustments made by a differentuser to a subset of the raw images. A correlation between the databaseof raw and adjusted images and the adjustments made by a different usermay be computed. The computed correlation, or transferred adjustment,may be applied to a new image to automatically globally adjust the newimage. In one embodiment, a new image may be automatically globallyadjusted based on a regression algorithm trained on the database of rawand adjusted images and an adjustment offset. The adjustment offset maybe determined by the trained regression algorithm predicting curves forthe small set of images, and computing the difference between thepredicted curves and adjustments the user made to the small set ofimages. A new image may be automatically globally adjusted by applying aweighted combination of the plurality of curves and the adjustmentoffset to the new image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example module that may implement an automaticimage adjustment method, according to some embodiments.

FIGS. 2A-2F show an example of a raw image and examples of correspondingadjusted images.

FIG. 3 illustrates a flowchart of an example method for globallyadjusting an image, according to some embodiments.

FIG. 4 illustrates a flowchart of an example method for globallyadjusting an image based on a user's adjustments, according to someembodiments.

FIG. 5 illustrates a flowchart of an example method for globallyadjusting an image by using the difference between a predictedadjustment and a user's adjustment, according to some embodiments.

FIG. 6 illustrates an example computer system that may be used inembodiments.

FIG. 7A illustrates an example categorization of an image database thatmay be used in various embodiments,

FIG. 7B illustrates a sample ranking of adjusted images that may be usedin various embodiments.

FIG. 8 shows prediction errors for several techniques of predicting auser's adjustment.

FIGS. 9-11 illustrate example prediction errors, according to variousembodiments.

While the disclosure is described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that the disclosure is not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit the disclosure tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present disclosure. The headings used herein arefor organizational purposes only and are not meant to be used to limitthe scope of the description. As used throughout this application, theword “may” is used in a permissive sense (i.e., meaning having thepotential to), rather than the mandatory sense (i.e., meaning must).Similarly, the words “include”, “including”, and “includes” meanincluding, but not limited to.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of claimed subject matter.However, it will be understood by those skilled in the art that claimedsubject matter may be practiced without these specific details. In otherinstances, methods, apparatuses or systems that would be known by one ofordinary skill have not been described in detail so as not to obscureclaimed subject matter.

Some portions of the detailed description which follow are presented interms of algorithms or symbolic representations of operations on binarydigital signals stored within a memory of a specific apparatus orspecial purpose computing device or platform. In the context of thisparticular specification, the term specific apparatus or the likeincludes a general purpose computer once it is programmed to performparticular functions pursuant to instructions from program software.Algorithmic descriptions or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processing orrelated arts to convey the substance of their work to others skilled inthe art. An algorithm is here, and is generally, considered to be aself-consistent sequence of operations or similar signal processingleading to a desired result. In this context, operations or processinginvolve physical manipulation of physical quantities. Typically,although not necessarily, such quantities may take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer or a similar special purpose electronic computingdevice. In the context of this specification, therefore, a specialpurpose computer or a similar special purpose electronic computingdevice is capable of manipulating or transforming signals, typicallyrepresented as physical electronic or magnetic quantities withinmemories, registers, or other information storage devices, transmissiondevices, or display devices of the special purpose computer or similarspecial purpose electronic computing device.

Various embodiments of automatic global image adjustment are described.In embodiments, a number of raw images and corresponding adjusted imagesmay be provided to an automatic image adjustment module. A supervisedlearning module may relate one or more parameters of each raw image toits corresponding adjusted image and generate a curve that describes theadjustments. In some embodiments, the supervised learning module maytrain a regression algorithm to perform a weighted combination of thecurves. The automatic image adjustment module may receive a new imageand globally adjust one or more parameters of the new image to create anadjusted new image. In one embodiment, the automatic image adjustmentmodule may apply the trained regression algorithm to the new image. Inone embodiment, the automatic image adjustment module may receive auser's adjustments to a subset of the raw images and compute atransferred adjustment, which may model the user's image adjustments.The transferred adjustment may then be applied to a new image. In oneembodiment, the automatic image adjustment module may compute adifference, or adjustment offset, between a user's adjustment to asubset of the raw images and the curves for each image of the subset ofimages. The trained algorithm, followed by the adjustment offset, maythen be applied to a new image.

Some embodiments may include a means for automatically adjusting animage. For example, an automatic image adjustment module may receiveimage pairs, including raw and adjusted images, and, in someembodiments, a user input, and may generate one or more algorithmscapable of automatically adjusting a new image, as described herein. Theautomatic image adjustment module may in some embodiments be implementedby a non-transitory, computer-readable storage medium and one or moreprocessors (e.g., CPUs and/or GPUs) of a computing apparatus. Thecomputer-readable storage medium may store program instructionsexecutable by the one or more processors to cause the computingapparatus to perform receiving image pairs, including raw and adjustedimages, and, in some embodiments, a user input, and may generate one ormore algorithms capable of automatically adjusting a new image, asdescribed herein. Other embodiments of the automatic image adjustmentmodule may be at least partially implemented by hardware circuitryand/or firmware stored, for example, in a non-volatile memory.

Turning now to FIG. 1, automatic image adjustment module 100 mayimplement one or more embodiments of automatic image adjustment, asdescribed herein. In the embodiment shown, automatic image adjustmentmodule 100 may receive image database 102, which may include image pairsof raw images 104 and corresponding adjusted images 106. Automatic imageadjustment module may also receive user input 108 (via user interface110) and new image 112, and may output an adjusted version of new image112, adjusted new image 120.

In one embodiment, image database 102 may include a plurality ofphotographs. For example, image database 102 may include 5000 raw images104, which may be the original RAW format images straight from thecamera (i.e., all the information recorded by the camera sensor isavailable) and may also include adjusted versions of the RAW images,adjusted images 106. The images may cover a variety of scenes, subjects,and lighting conditions. For example, the dataset of raw images 102 mayinclude difficult cases such as images with back-lighting and foggyscenes. In one embodiment, the image resolution of raw images 102 mayrange from 6 to 21 megapixels. The dataset may contain photos from avariety of different cameras and lenses. Raw images 102 may includeimages taken using automatic exposure and may also include images takenin which the exposure was manually set. In one embodiment, imagedatabase 102 may include multiple sets of adjusted images 106. Forexample, image database 102 may include five sets of 5000 adjustedimages 106 that correspond to the 5000 raw images 104. In this example,an image pair may be a raw image 104 and one of the correspondingadjusted images 106 from any of the five sets. One example of a type ofadjustment made to raw images 104 that may be reflected in adjustedimages 106 includes tonal adjustments. Adjusted images 106 may be theresult of adjusting raw images 104 with image editing or retouchingsoftware. This software may provide an interface composed of severalsliders and a tone curve to perform adjustments, such as brightness,contrast, exposure, and black level. In one embodiment, adjusted images106 may be adjusted using sliders of retouching software that correspondto a global remapping of pixel values. This may include fourteencontrols for the tone curve and two controls for the white balance. Inaddition, adjusted images 106 may also include adjustments made by usinga slider that selectively brightens shadow regions of an image. Anexample of a raw image 104 and corresponding adjusted images 106 areshown in FIGS. 2A-F. An example raw image 104 is shown in FIG. 2B andfive corresponding adjusted images 106 are shown in FIGS. 2B through 2F.In the example shown in FIG. 2, five retouchers have produced diverseadjusted versions (FIGS. 2B through 2F) from a sunset mood (FIG. 2B) toa daylight look (FIG. 2F).

One example of a categorization of image database 102, which may be usedin various embodiments, is illustrated in FIG. 7A. In one embodiment, atool (electronic, human, or otherwise) may be used to assign categoriesto the images. In the illustrated example, each photo has beencategorized according to the following categories: scene, time of day,light, and subject. Each category may also include multiplesubcategories (e.g., day, dusk, dawn, night, hard to tell for “time ofday”). Other image databases 102 may include a different categoricalbreakdown of images. FIG. 7B illustrates a sample ranking of sets ofcorresponding adjusted images of one or more raw images 104 that may beused in various embodiments. 100% may correspond to a more favorable setof adjusted images while 0% may correspond to a less favorable set. Forexample, the photographs of FIGS. 2B-2F may each be one adjusted imagein different sets of adjusted images (e.g., the photograph of FIG. 2Bbelongs to a set A of retoucher A, the photograph of FIG. 2C belongs toa set B of retoucher B, etc.). The sets of adjusted images arerepresented in FIG. 7B by A-E and may correspond to a collection ofimages adjusted by a single retoucher or group of retouchers. The setsA-E, may fall somewhere on a ranking scale. As shown in FIG. 7B, setsthat include adjusted images shown in FIGS. 2D and 2F (sets/retouchers Cand E) may belong to sets with favorable retouching than the set thatthe adjusted image of FIG. 2C belongs to (set/retoucher B).

Referring back to FIG. 1, in various embodiments, image database 102 maybe included as part of automatic image adjustment module 100 and not asa stand alone database. Further, multiple image databases 102 may bepresent. The databases may include a black and white database or an HDRdatabase. Image database 102 may allow automatic image adjustment module100 to enable supervised learning to learn global adjustments, such asglobal tonal adjustments, as opposed to techniques that seek to trainfrom only adjusted images. In one embodiment, image database 102 mayallow automatic image adjustment module 100 to learn image adjustmentpreferences of a new user from adjustments made to a small set ofimages. In various embodiments, image database 102 may allow automaticimage adjustment module 100 to predict a difference in image adjustmentpreference for a new user.

In one embodiment, automatic image adjustment module 100 may receiveuser input 108 via user interface 110. User interface 110 may include akeyboard, touch screen device, microphone, or pointing device (e.g.,mouse, trackball, stylus, or other similar devices). In one embodiment,user input 108 may include adjustments made with sliders that correspondto global adjusting of pixel values. User input 108 may include othertypes of adjustments as well. Adjustments made by user input 108 may beto images of image database 102 or to other images.

In one embodiment, new image 112 may be an image, not contained in imagedatabase 102, to which a global adjustment is made to its pixels. Theglobal adjustment made to the pixels of new image 112 may result inadjusted new image 120. New image 112 may be taken with a differentcamera and lens combination than the images of image database 102 andmay be of any subject matter, scene, and under any conditions or camerasettings.

In one embodiment, automatic image adjustment module 100 may includesupervised learning module 114. Supervised learning module 114 may learnadjustments made by a photographer given a collection of image pairsthat may include raw images 102 and adjusted images 106. In oneembodiment, adjustments to an image may be represented as a remappingcurve from input luminance to output luminance, using the CIE-Lab colorspace for its reasonably perceptual uniformity. If the image data is RGBdata, it may be converted to the luminance color space and the curve maybe determined by comparing the original image data to adjusted imagedata. The curve may be global such that every pixel is treated the sameway. In one embodiment, each remapping curve may be represented by aspline with 51 uniformly sampled control points. The spline may be fitto pairs of input-output luminance values in a least-squares sense.Focusing on a select number of control points may allow for a morecompact representation of adjustments that may include millions ofinputs and outputs. In one embodiment, the exposure may be normalized tothe same baseline by linearly remapping the luminance values of eachimage such that the minimum luminance value may be 0 and the maximum maybe 100. In one embodiment, each learning curve may be approximated byusing the first principal component analysis (PCA) coefficient. This mayallow each curve to be summarized with a single number.

In various embodiments, features of the images included in the remappingcurves (and spline) may be represented by descriptors. Descriptors maybe computed using various techniques. The features used in supervisedlearning module 114 may range from low level descriptions of luminancedistribution to high-level aspects such as face detection. Features mayinclude intensity distributions, scene brightness, equalization curves,detail-weighted equalization curves, highlight clipping, spatialdistributions, and faces. In one embodiment, before computing features,the images may be resized such that their long edge is 500 pixels.

In one embodiment, supervised learning module 114 may use the feature ofintensity distributions. Photographers may rely on the distribution ofintensities as depicted by a log-scale histogram to adjust the tonalbalance. The mean of the distribution of the log-intensity log (R+G+B)may be computed with its percentiles sampled every 2%. Further, the samepercentiles may be evaluated on two Gaussian-convoluted versions of thephoto (σ=10 and σ=30) to account for tonal distributions at largerscales. In one embodiment, the image may be blurred and percentiles maybe computed a second time to simulate as if one was looking at the imagefrom farther away.

In one embodiment, supervised learning module 114 may use the feature ofscene brightness. Dark and bright scenes may be adjusted differently.Accordingly, scene brightness may be evaluated with (Ŷ×N²)/(Δt×ISO),where Ŷ is the median intensity, N is the lens aperture number that isinversely proportional to the aperture radius, Δt is the exposureduration, and ISO is the sensor gain. Settings from the camera such asthe lens aperture number may be obtained from image metadata. Scenebrightness may be proportional to the light power reaching the camerasensor and may assume that no filter is attached.

Supervised learning module 114 may also use equalization curves.Histogram equalization may allow a coarse approximation of the entireavailable intensity range. In one embodiment, the cumulativedistribution function (CDF) of the image intensities may be computed foreach image and projected onto the first five PCA components.

In one embodiment, supervised learning module 114 may use the feature ofdetail-weighted equalization curves. Detailed regions of images mayreceive more attention. As a result, supervised learning module 114 mayuse this feature. Detail-weighted equalization curves may be representedby weighting each pixel by the gradient magnitude, and then computingthe first five PCA coefficients of the CDF. The gradients may beestimated with Gaussian derivatives for σ=1, σ=100, and σ=200. This mayaccount for details at different scales.

In one embodiment, supervised learning module 114 may use the feature ofhighlight clipping. Highlight clipping may measure the amount ofhighlight that gets clipped. The label values that clip 1%, 2%, 3%, 5%,10%, and 15% of the image may be computed.

In one embodiment, supervised learning module 114 may use the feature ofspatial distributions. This may include the fraction of highlights,midtones, and shadows and how a given tone range is spatiallydistributed. The intensity range may be split into 10 intervals. Foreach interval, a 2D spatial Gaussian may be fit to the correspondingpixels. The feature value may be the area of the fitted Gaussian dividedby the number of pixels. The xy coordinates of the center of theGaussian may also be used as a feature that represents the coarsespatial distribution of tones.

Supervised learning module 114 may use the feature of faces in variousembodiments. Faces may be a main subject of photographs and theadjustment of faces may be a priority over other content. Further, faceadjustment may follow different guidelines than other content. Faces maybe detected and the following features may be computed: intensitypercentiles within facial regions (if none, the percentiles of the wholeimage may be used), total area, mean xy location, and number of faces).Any type of face detector may be used. In various embodiments,supervised learning module 114 may use other features such as localhistograms, color distributions, and scene descriptors.

By describing the images in terms of features, supervised learningmodule 114 may use the features to learn how adjustments are made toimages, without regard to individual pixels. In one embodiment, thecomputed features may include 266 numbers for each image.

In one embodiment, supervised learning module 114, given image database102, may learn to adjust images similarly to the adjustments of adjustedimages 106. In one embodiment, supervised learning module 114 may learnglobal tonal adjustment, which may not include hue, saturation, color,vibrance, or white balance changes, such that the luminance of an inputpixel applied to a remapping curve may give the luminance of the outputpixel. For example, adjustments to the luminance channel may includechanges to aspects including: black points, highlights, shadows,contrast, brightness and exposure. The data from the curves may beprocessed by a regression algorithm, such as linear regression,least-squares regression (LSR), least absolute shrinkage and selectionoperator (LASSO) regression, or Gaussian Processes Regression (GPR). Inone embodiment, LASSO may perform a linear regression on a sparse subsetof the input dimensions and the algorithm may be trained using 5-foldcross-validation on the training set. Using a regression algorithm mayallow supervised learning module 114 to describe image adjustments witha single number. Using a regression algorithm to describe imageadjustments may be described as training the algorithm. Imageadjustments may be described for the entire image database 102 or for asubset of the images, or training set. The result of the regressionalgorithm may be a formula that may be used to predict and make imageadjustments to new images 112. In one embodiment, the analysis performedby the regression algorithm on the descriptor vectors may result in afirst PCA number for the curve of each image.

FIG. 8 is a table that illustrates the error in predicting a user'sadjustment using a variety of techniques. The calculated values in FIG.8 represent predicting a user's adjustment based on a set of adjustedimages 106 that were retouched in a similar manner to FIG. 2D. Forreference, a lower error value means less error. The variety oftechniques used include: no adjustment at all, i.e., using the identityas remapping curve; the commercial software Google® Picasa®, which mayalso used by a conventional method (this tool may not use image database102); the mean curve of the testing set; metric-learning using 25sensors; least-squares regression (LSR); LASSO set to keep about 50features; GPR; and a leave-one-out performance of metric-learning. Asshown, regression techniques may perform significantly better, in termsof error, than other approaches.

Turning back to FIG. 1, as an example embodiment using GPR, adjustmentsto new image 112 may proceed in two steps, with the first stepcorresponding to training the algorithm. During training, GPR mayoptimize the hyper-parameters of a covariance function so that it mayexplain the training set. Then, when predicting adjustments to new image112, known as run-time, the covariance function may be used to drive thecombination of a selection of some of the training curves. In oneembodiment, given a new image 112, 266 numbers representing features ofthe new image may be computed and fed to GPR. In one embodiment, theselection of training curves may include selecting several nearestneighbors and combining them in a weighted combination. For example,curves of images 1, 14, 15, 16, 94, 104, 1603, and 2300 may be combinedat weights 1%, 3%, 3%, 1%, 35%, 0.3%, 7%, and 49.7% respectively for aparticular new image 112. For other new images 112, the nearestneighbors and weights may be different. In one embodiment, each trainingcurve may be represented for a given new image 112. For example, if atraining set includes 2500 images, each of the 2500 images may beselected and combined but many of the curves may be weighted by anear-zero percentage, such as 0.001% so that the nearest neighbors maybe weighted more heavily and non-nearest neighbors may not be weightedheavily. In one embodiment, training curves with a weight below acertain threshold may be clipped to simplify and expedite computation.Other techniques to generate a curve for new image 112 may be used aswell. For example, supervised learning module 114 may find a similarimage to new image 112 in image database 102 and apply its curve to newimage 112. In one embodiment, when predicting an adjustment to new image112, a full curve may be used to globally adjust the new image, not asimplified curve that includes only the first PCA component. In otherwords, in one embodiment, training the algorithm may use just the firstPCA coefficient while predicting using the trained algorithm may use afull set of curve parameters. In various embodiments, the algorithm maybe trained in another component and not by supervised learning module114. In such embodiments, the pre-trained algorithm may be used bysupervised learning module 114 to adjust new images.

FIG. 9 illustrates example prediction errors using one embodiment of atrained covariance function. One example represents a GPR covariancetrained on a training set of 2500 images but using only a small number(n) of curves at run-time for prediction. Another example represents aGPR covariance trained with only n images and using the same n imagesfor prediction, which, as a result, may reduce the size of the trainingset. Thus, the first example utilizes a rich training set while thesecond example uses a small training set. The two examples show thatusing a well-trained covariance function may yield better predictionresults given the same small number of run-time data. As shown in FIG.9, the well-trained covariance function may predict, with the sameamount of error, learning from only 10 photos that the lesser-trainedcovariance function may predict learning from 30 photos. FIG. 9 alsoshows a baseline covariance trained on a set of 2500 images and usingall 2500 curves at run-time for prediction. This is seen as thehorizontal line just below a 5 prediction error.

Referring back to FIG. 1, in one embodiment, automatic image adjustmentmodule 100 may include transferred adjustment module 116. Transferredadjustment module 116 may learn the adjustments of a new user. The newuser may adjust a small set of images S, for example, from imagedatabase 102. In one embodiment, S may include tens of images for a userto adjust. For example, S may include 25 images. The small set of imagesS that the new user adjusts may be a subset of image database 102. Inone embodiment, the small set of images S may be the most useful photosof the image database 102 to learn a new user's adjustment. The smallset of images S may be selected with a sensor placement technique. Inone embodiment, GPR may be run on a large set L of image pairs fromimage database 102 to compute a covariance function Σ_(L). This may beperformed by supervised learning module 114. The covariance functionΣ_(L) that is trained on L may be used by GPR to run an interpolation onthe curves of the small set S. In one embodiment, the curves of thesmall set S may be computed by GPR in the same manner the curves werecomputed for the images of large set S. Given new image 112, GPR mayproduce weights for the curves of the small set of images S. Descriptorsmay be computed for new image 112 and then weighted and combinedaccordingly to generate adjusted new image 120. The computation andweighting may collectively be referred to, in some embodiments, as thetransferred adjustment.

FIGS. 10A-10E illustrate performance in predicting an adjustment using asmall set of images S and a large set L, according to some embodiments.FIG. 10F is a summary illustration of FIGS. 10A-10E. In each FIG.10A-10F, the prediction accuracy as a function of size S is shown. EachFIG. 10A-10E includes a plot of the accuracy for sensor placementselection (solid line), for a random selection (average in thedash-dotted black line, up to one standard deviation in gray), and of aleave-one-out test (dashed gray line). The figures illustrate animprovement in the prediction when utilizing one or more of: imagedatabase 102, which may include subsets S and L; metric learning withGPR; or sensor placement. The improvement may be even greater for largevalues of S.

In one embodiment, automatic image adjustment module 100 may includedifference learning module 118. Difference learning module 118 may learnthe difference in a new user's adjustment preferences over a referencepredicted adjustment. In one embodiment, difference learning module 118may use only a few images that may be arbitrarily selected. Further, thefew images may include images that are not included in image database102. If the images are included in image database 102, sensor placementor other such techniques may not be needed to optimize differencelearning module 118. However, in some embodiments, difference learningmodule 118 may use such techniques to optimize which images a new useradjusts. By randomly choosing pictures for training, difference learningmodule 118 may learn adjustment preferences on-the-fly. Given new image112, difference learning module 118 may predict both a referenceadjustment and the difference between the reference adjustment and thenew user's adjustment. In other words, difference learning module 118may apply a predictive adjustment followed by a predictive correction togenerate adjusted new image 120. In some embodiments, the referencepredictive adjustment may be determined by supervised learning module114, as described herein.

In one embodiment, GPR may be trained on a large training set L ofimages, such as image database 102. Then, reference curves for eachphoto of a small set of images S may be predicted. The differencebetween the predicted curves of S and the new user's curves for thoseimages may be computed. The computed differences yield a series ofadjustment offsets, o. Given new image 112, a reference adjustment r maybe predicted using the covariance Σ_(L) and the adjustments in L. Then,an adjustment offset o may be predicted using the L covariance Σ_(L) andthe offsets computed on S. The adjustment offset o may be added to thereference adjustment r such that for new image 112, a combinedadjustment r+o may be applied to new image 112 resulting in adjustmentnew image 120.

FIG. 11 illustrates example prediction errors based on a number ofsample photographs in a sample set, according to various embodiments.The bottom two plots of FIG. 11 represent lower prediction errors andmay be predictions made according to embodiments of difference learningmodule 118. As shown in the bottom two plots, as few as 3 samplephotographs (n=3) may yield better results. Also shown, in thebottom-most plot, selecting photographs with sensor placement, inconjunction with difference learning, may yield improved prediction withas few as two example photographs.

Using supervised learning to predict image adjustments may predictadjustments to new images better than image adjustment techniques thatrely on a set of rules, (e.g., if the right side of the image is dark,make it brighter) or only on unsupervised learning (only using theadjusted images and not the pre-adjusted images). The disclosedtechniques may analyze adjustments that a photographer or user has madeand predict adjustments to new images based on that analysis.

Turning now to FIG. 3, one embodiment of a method of automaticallyglobally adjusting a new image is shown. In one embodiment, automaticimage adjustment module 100 may perform the method of FIG. 3. While theblocks are shown in a particular order for ease of understanding, otherorders may be used. In some embodiments, the method of FIG. 3 mayinclude additional (or fewer) blocks than shown.

At 302, automatic image adjustment module 100 may receive a plurality ofimage pairs. In one embodiment, image pairs may include a raw image 104and a corresponding adjusted image 106. Adjusted image 106 is said tocorrespond to raw image 104 because it is an adjusted version of the rawimage. Automatic image adjustment module 100 may receive, in oneembodiment, 5000 image pairs. Automatic image adjustment module 100 mayreceive additional sets of adjusted images 106 that correspond to rawimages 104.

At 304, automatic image adjustment module 100 may generate a pluralityof training curves by training a regression algorithm. Each of theplurality of curves may relate one or more parameters of a raw image 104to a corresponding adjusted image 106 of an image pair. The parametersthat the plurality of curves may be based upon may include tonalparameters such as black points, highlights, shadows, contract,brightness and exposure. The parameters may be described in terms offeatures such as intensity distributions, scene brightness, equalizationcurves, detail-weighted equalization curves, highlight clipping, spatialdistributions, and faces. In one embodiment, a regression algorithm, forexample, a GPR algorithm, may be used to compute a training curve foreach image pair by analyzing 266 descriptor vectors, given by thedescribed features. Each training curve may be represented with a firstPCA coefficient. The plurality of curves may be seen as hyper-parametersof a covariance function that may explain the training set image pairs.

At 306, automatic image adjustment module 100 may receive new image 112.New image 112 may be an image not contained in image database 102. Newimage 112 may be taken with a different camera and lens combination thanthose images of image database 102 and may be of any subject matter,scene, and under any conditions or camera settings.

At 308, automatic image adjustment module 100 may globally adjust one ormore parameters of new image 112. In one embodiment, globally adjustingone or more parameters of new image 112 may result in adjusted new image120. In one embodiment, the trained regression algorithm, for example,GPR, may use the covariance function computed in block 304 to drive acombination of training curves to optimize adjustment of a new image.The training curves may be combined in a weighted manner. In oneembodiment, the selection of training curves may include selectingseveral nearest neighbors and combining them in a weighted combination.Further, the curves used to globally adjust one or more parameters ofnew image 112 may include the fully array of PCA coefficients, and notjust the first coefficient. The composite weighted training curve may beapplied globally to the luminance of an input pixel to determine theluminance of an output pixel. In some embodiments, performing block 308may result in adjusted new image 120.

Turning now to FIG. 4, one embodiment of a method of automaticallyglobally adjusting a new image based on a new user's adjustments isshown. In one embodiment, automatic image adjustment module 100 mayperform the method of FIG. 4. While the blocks are shown in a particularorder for ease of understanding, other orders may be used. In someembodiments, the method of FIG. 4 may include additional (or fewer)blocks than shown.

In one embodiment, the method of FIG. 4 may include blocks 302-306 ofthe method of FIG. 3. At 402, automatic image adjustment module 100 mayreceive adjustments to a subset of images. The subset of images may be asubset of image database 102. In one embodiment, the subset of imagesmay be selected such that the subset of images is the most useful tolearn a new user's adjustment preferences. In one embodiment, the subsetof images may be selected with a sensor placement technique. The subsetof images may in the range of tens of images, for example, 20 to 30images.

At 404, automatic image adjustment module 100 may compute a transferredadjustment by correlating the trained regression algorithm to theadjustment of the subset of images. In one embodiment, curves of thesubset of images may be computed by a regression algorithm, such as GPR,in a similar manner to the computation of the curves of the larger setof images. In one embodiment, the covariance function trained on L, fromblock 304 of FIG. 3, may be used to run GPR interpolation on the curvesof the subset of images resulting in the transferred adjustment.

At 406, automatic image adjustment module 100 may globally adjust one ormore parameters of new image 112. In one embodiment, the transferredadjustment that results from the GPR interpolation on the subset ofimages may be applied to new image 112. Given new image 112, GPR mayproduce weights for the curves of the subset of images. In oneembodiment, descriptors may be computed for new image 112 and thenweighted and combined accordingly. In some embodiments, performing block406 may result in adjusted new image 120.

Turning now to FIG. 5, one embodiment of a method of automaticallyglobally adjusting a new image based on the difference between a newuser's adjustments and predicted adjustments is shown. In oneembodiment, automatic image adjustment module 100 may perform the methodof FIG. 5. While the blocks are shown in a particular order for ease ofunderstanding, other orders may be used. In some embodiments, the methodof FIG. 5 may include additional (or fewer) blocks than shown.

In one embodiment, the method of FIG. 5 may include blocks 302-306 ofthe method of FIG. 3. At 502, automatic image adjustment module 100 mayreceive an adjustment to a subset of images. The subset of images may bea subset of image database 102 or it may be a set of images not includedin image database 102. The subset of images may include a few images(e.g., 2 or 3). The adjustments to the subset of images may berepresented by descriptor vectors. For example, in one embodiment, theadjustments may be represented by 266 values.

At 504, automatic image adjustment module 100 may compute an adjustmentoffset. The adjustment offset may be the difference between theadjustment to the subset of images and the curves for each image. In oneembodiment, reference curves for each image of the subset of images maybe predicted. The difference between the predicted curves of the subsetof images and the new user's curves (which may be computed from thedescriptor vectors) for those images may be computed. The computeddifferences may yield a series of adjustment offsets, o.

At 506, automatic image adjustment module 100 may globally adjust one ormore parameters of new image 112. Globally adjusting one or moreparameters of new image 112 may include the trained regression algorithmperforming a weighted combination of the plurality of curves to newimage 112 resulting in a predictive adjustment. The adjustment offset(predictive correction) may then be applied to the predictiveadjustment. In some embodiments, performing block 506 may result inadjusted new image 120.

Example System

Embodiments of automatic image adjustment techniques may be executed onone or more computer systems, which may interact with various otherdevices. One such computer system is illustrated by FIG. 6. In differentembodiments, computer system 600 may be any of various types of devices,including, but not limited to, a personal computer system, desktopcomputer, laptop, notebook, or netbook computer, mainframe computersystem, handheld computer, workstation, network computer, a camera, aset top box, a mobile device, a consumer device, video game console,handheld video game device, application server, storage device, aperipheral device such as a switch, modem, router, or in general anytype of computing or electronic device.

In the illustrated embodiment, computer system 600 includes one or moreprocessors 610 coupled to a system memory 620 via an input/output (I/O)interface 630. Computer system 600 further includes a network interface640 coupled to I/O interface 630, and one or more input/output devices650, such as cursor control device 660, keyboard 670, and display(s)680. In some embodiments, it is contemplated that embodiments may beimplemented using a single instance of computer system 600, while inother embodiments multiple such systems, or multiple nodes making upcomputer system 600, may be configured to host different portions orinstances of embodiments. For example, in one embodiment some elementsmay be implemented via one or more nodes of computer system 600 that aredistinct from those nodes implementing other elements.

In various embodiments, computer system 600 may be a uniprocessor systemincluding one processor 610, or a multiprocessor system includingseveral processors 610 (e.g., two, four, eight, or another suitablenumber). Processors 610 may be any suitable processor capable ofexecuting instructions. For example, in various embodiments, processors610 may be general-purpose or embedded processors implementing any of avariety of instruction set architectures (ISAs), such as the x86,PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. Inmultiprocessor systems, each of processors 610 may commonly, but notnecessarily, implement the same ISA.

In some embodiments, at least one processor 610 may be a graphicsprocessing unit. A graphics processing unit or GPU may be considered adedicated graphics-rendering device for a personal computer,workstation, game console or other computing or electronic device.Modern GPUs may be very efficient at manipulating and displayingcomputer graphics, and their highly parallel structure may make themmore effective than typical CPUs for a range of complex graphicalalgorithms. For example, a graphics processor may implement a number ofgraphics primitive operations in a way that makes executing them muchfaster than drawing directly to the screen with a host centralprocessing unit (CPU). In various embodiments, automatic imageadjustment methods disclosed herein may, at least in part, beimplemented by program instructions configured for execution on one of,or parallel execution on two or more of, such GPUs. The GPU(s) mayimplement one or more application programmer interfaces (APIs) thatpermit programmers to invoke the functionality of the GPU(s). SuitableGPUs may be commercially available from vendors such as NVIDIACorporation, ATI Technologies (AMD), and others.

System memory 620 may be configured to store program instructions and/ordata accessible by processor 610. In various embodiments, system memory620 may be implemented using any suitable memory technology, such asstatic random access memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementingdesired functions, such as those described above are shown stored withinsystem memory 620 as program instructions 625 and data storage 635,respectively. In other embodiments, program instructions and/or data maybe received, sent or stored upon different types of computer-accessiblemedia or on similar media separate from system memory 620 or computersystem 600. Generally speaking, a computer-accessible medium may includestorage media or memory media such as magnetic or optical media, e.g.,disk or CD/DVD-ROM coupled to computer system 600 via I/O interface 630.Program instructions and data stored via a computer-accessible mediummay be transmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link, such asmay be implemented via network interface 640.

In one embodiment, I/O interface 630 may be configured to coordinate I/Otraffic between processor 610, system memory 620, and any peripheraldevices in the device, including network interface 640 or otherperipheral interfaces, such as input/output devices 650. In someembodiments, I/O interface 630 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 620) into a format suitable for use byanother component (e.g., processor 610). In some embodiments, I/Ointerface 630 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 630 may be split into two or more separate components, such asa north bridge and a south bridge, for example. In addition, in someembodiments some or all of the functionality of I/O interface 630, suchas an interface to system memory 620, may be incorporated directly intoprocessor 610.

Network interface 640 may be configured to allow data to be exchangedbetween computer system 600 and other devices attached to a network,such as other computer systems, or between nodes of computer system 600.In various embodiments, network interface 640 may support communicationvia wired or wireless general data networks, such as any suitable typeof Ethernet network, for example; via telecommunications/telephonynetworks such as analog voice networks or digital fiber communicationsnetworks; via storage area networks such as Fibre Channel SANs, or viaany other suitable type of network and/or protocol.

Input/output devices 650 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 600. Multipleinput/output devices 650 may be present in computer system 600 or may bedistributed on various nodes of computer system 600. In someembodiments, similar input/output devices may be separate from computersystem 600 and may interact with one or more nodes of computer system600 through a wired or wireless connection, such as over networkinterface 640.

As shown in FIG. 6, memory 620 may include program instructions 625,configured to implement embodiments as described herein, and datastorage 635, comprising various data accessible by program instructions625. In one embodiment, program instructions 625 may include softwareelements of embodiments as illustrated in the above Figures. Datastorage 635 may include data that may be used in embodiments. In otherembodiments, other or different software elements and data may beincluded.

Those skilled in the art will appreciate that computer system 600 ismerely illustrative and is not intended to limit the scope of anautomatic image adjustment module as described herein. In particular,the computer system and devices may include any combination of hardwareor software that can perform the indicated functions, including acomputer, personal computer system, desktop computer, laptop, notebook,or netbook computer, mainframe computer system, handheld computer,workstation, network computer, a camera, a set top box, a mobile device,network device, internet appliance, PDA, wireless phones, pagers, aconsumer device, video game console, handheld video game device,application server, storage device, a peripheral device such as aswitch, modem, router, or in general any type of computing or electronicdevice. Computer system 600 may also be connected to other devices thatare not illustrated, or instead may operate as a stand-alone system. Inaddition, the functionality provided by the illustrated components mayin some embodiments be combined in fewer components or distributed inadditional components. Similarly, in some embodiments, the functionalityof some of the illustrated components may not be provided and/or otheradditional functionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 600 may be transmitted to computer system600 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Accordingly, the present disclosure may bepracticed with other computer system configurations.

CONCLUSION

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent example embodiments of methods. The methods may be implementedin software, hardware, or a combination thereof. The order of method maybe changed, and various elements may be added, reordered, combined,omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended that the disclosure embrace all such modifications and changesand, accordingly, the above description to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method comprising;receiving, by a computing device, one or more adjustments made by a userto one or more parameters of each raw image in a subset of a pluralityof raw images, each raw image in the subset of the plurality of rawimages corresponding to an image pair that includes the raw image and anadjusted version of the raw image having one or more adjusted parametersin comparison to one or more unadjusted parameters of the raw image;computing, by the computing device, a transferred adjustment that isconfigured to model the one or more adjustments made by the user andwhich is usable for subsequent automatic adjustment of new images;computing an adjustment offset that defines a difference between thetransferred adjustment and a plurality of curves that describe the oneor more adjusted parameters corresponding to each image pair; receiving,by the computing device, a new image; and automatically globallyadjusting, by the computing device, the new image by at least applyingthe transferred adjustment and the adjustment offset to the new image tocreate an adjusted new image that is based on the one or more modeledadjustments.
 2. The computer-implemented method as described in claim 1,further comprising: receiving a plurality of image pairs that includethe plurality of raw images and a plurality of corresponding adjustedimages; generating the plurality of curves to relate, for each imagepair, the one or more adjusted parameters of the adjusted version of theraw image to the one or more corresponding unadjusted parameters of theraw image; and training a regression algorithm to perform a weightedcombination of the plurality of curves.
 3. The computer-implementedmethod as described in claim 2, further comprising: applying the trainedregression algorithm to the new image; and subsequent to said applyingthe trained regression algorithm to the new image, applying theadjustment offset to the new image.
 4. The computer-implemented methodas described in claim 1, wherein the transferred adjustment is computedby at least correlating a trained regression algorithm to the one ormore adjustments made by the user to the subset of the plurality of rawimages.
 5. The computer-implemented method as described in claim 4,wherein the trained regression algorithm is based on a plurality ofcurves that relate one or more unadjusted parameters of the raw image toone or more adjusted parameters of an adjusted version of the raw image.6. The computer-implemented method as described in claim 1, wherein thenew image comprises a new raw image captured by an image capturingdevice.
 7. A non-transitory computer readable storage device storingprogram instructions that are executable by a computer to cause thecomputer to perform a method comprising: applying a predictiveadjustment to a new image based on a reference predicted adjustment, thereference predicted adjustment being based on a plurality of curves thateach relate one or more unadjusted parameters of a raw image capturedfrom an image capturing device to one or more adjusted parameters of anadjusted image of an image pair; computing a predictive correction thatdefines a difference between the reference predictive adjustment andadjustment preferences of a user, the adjustment preferences of the userbeing based on one or more adjustments made by the user to one or moreparameters of each raw image in a set of raw images; and applying thepredictive correction to the new image, subsequent to the predictiveadjustment being applied, to generate an adjusted new image.
 8. Thenon-transitory computer readable storage device as described in claim 7,wherein the predictive adjustment and the predictive correction areglobally applied to one or more parameters of the new image.
 9. Thenon-transitory computer readable storage device as described in claim 7,wherein the adjusted image of the image pair comprises a result ofadjusting the raw image with image editing software.
 10. Thenon-transitory computer readable storage device as described in claim 7,wherein the raw image comprises unaltered raw image data captured fromthe image capturing device.
 11. The non-transitory computer readablestorage device as described in claim 7, wherein the predictiveadjustment is based on a weighted combination of the plurality ofcurves.
 12. A system comprising: one or more processors and at least onememory that maintains instructions that are executable by the one ormore processors to cause operations to be performed comprising:receiving one or more adjustments to each raw image of a subset of rawimages of a plurality of image pairs, each said image pair comprising araw image and an adjusted version of the raw image having one or moreadjusted parameters in comparison to one or more correspondingunadjusted parameters of the raw image, computing a transferredadjustment by correlating a trained regression algorithm to the one ormore adjustments of the subset of raw images; computing an adjustmentoffset that defines a difference between the transferred adjustment andone or more adjustment preferences of a user; and automatically globallyadjusting one or more parameters of a new image by at least applying thetransferred adjustment and the adjustment offset to the new image tocreate an adjusted new image.
 13. The system as recited in claim 12,wherein the transferred adjustment is computed at least by generating aplurality of curves that relate, for each image pair, the one or moreadjusted parameters of the adjusted version of the raw image to the oneor more corresponding unadjusted parameters of the raw image.
 14. Thesystem as recited in claim 13, wherein the trained regression algorithmis configured to produce weights for the plurality of curves.
 15. Thesystem as recited in claim 12, wherein the new image comprises raw imagedata captured by an image capturing device.
 16. The system as recited inclaim 12, wherein the one or more adjustments to the subset of rawimages are received responsive to a user input that causes the one ormore adjustments to be applied to one or more parameters of the subsetof raw images.
 17. The system as recited in claim 12, wherein thetrained regression algorithm is a Gaussian Processes Regression (GPR)algorithm.
 18. The system as recited in claim 12, wherein the one ormore parameters include tonal parameters.
 19. The system as recited inclaim 12, wherein the adjustment offset is applied subsequent toapplication of the transferred adjustment.
 20. The system as recited inclaim 12, wherein the adjustment offset is based on a weightedcombination of a plurality of curves that describe the one or moreadjusted parameters of the adjusted version of the raw image.